Modeling of drying and rehydration kinetics of<scp><i>Rosa pimpinellifolia</i></scp>fruits: Toward formulation and optimization of a new tea with high antioxidant properties

Pashazadeh, Hojjat; Zannou, Oscar; Koca, İlkay

doi:10.1111/jfpe.13486

Cited by 23 publications

(40 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our results are consistent with previous studies which have reported these models to be adequate to predict the rehydration behavior of food products (Benseddik et al, 2019; Dadali, Demirhan, & Özbek, 2008; Goula & Adamopoulos, 2009; Noshad, Mohebbi, Shahidi, & Mortazavi, 2012). In addition, Pashazadeh et al (2020) have reported that Peleg, Vegá‐galves and Weibull models depicted better the rehydration behavior of black rosehip at 20, 40, and 60°C.…”

Section: Resultsmentioning

confidence: 96%

“…The width and length of the fresh fruits were determined with a micrometer and the water activity was measured with a water activity meter (Aqualab 4TE). The total soluble solids were determined by homogenizing 10 g of crushed fruit with 20 ml of distilled and the mixture was read with a digital refractometer at room temperature (20°C; Pashazadeh, Zannou, & Koca, 2020). The pH was directly measured in mixture prepared for total soluble solids using a glass electrode pH meter.…”

Section: Methodsmentioning

confidence: 99%

“…The drying system was a laboratory‐scale cabinet dryer (EKSIS, Turkey) functioning in the convection regime. It has been successfully used to investigate the drying kinetics of strawberry (Doymaz, 2008) and black rosehip (Pashazadeh et al, 2020). The drying process was optimized using the one‐factor response surface methodology (Design‐Expert Software 11.0 Trial version, Stat‐Ease Inc., Mineapolis).…”

Section: Methodsmentioning

confidence: 99%

“…The models with higher R 2 along with the lowest χ 2 and RMSE were the best models to predict the dehydration kinetics. These parameters were identified with Equations 3, 4, and 5 (Doymaz, 2008; Pashazadeh et al, 2020):

R^{2} = 1 - ((), \frac{\sum_{i = 1}^{N} {((), {MR}_{p, i} - {MR}_{e, i})}^{2}}{\sum_{\dot{normalI} = 1}^{N} {((), italicMRe, i - {\overset{true¯}{italicMR}}_{e, i})}^{2}})

χ^{2} = \frac{\sum_{i = 1}^{N} {({italicMR}_{e, i} - {italicMR}_{p, i})}^{2}}{N - n}

RMSE = {(\frac{1}{N} \sum_{i = 1}^{N} {((), {MR}_{e, i} - {MR}_{p, i})}^{2})}^{1 / 2}

where MR e , i and MR p , i represent the i th experimental and predicted moisture ratios, respectively.

{\overset{false¯}{italicMR}}_{e, i}

is the mean of the experimental moisture ratio.…”

Section: Methodsmentioning

confidence: 99%

“…The data of rehydration kinetics were statistically analyzed based on the values of coefficient of determination ( R 2 ), chi‐square (χ 2 ), and root mean square error (RMSE) using MATLAB software (R2016d). The models with higher R 2 , as well as the lowest χ 2 and RMSE were selected as the best models to interpret the rehydration behaviors of dried fruit (Pashazadeh et al, 2020).

R^{2} = 1 - \frac{\sum_{i = 1}^{N} {(R_{re, i} - R_{rp, i})}^{2}}{\sum_{i = 1}^{N} {({\overset{R}{true}}_{re, i} - R_{re, i})}^{2}}

χ^{2} = \frac{\sum_{i = 1}^{N} {(R_{re, i -} R_{rp, i})}^{2}}{N - n}

italicRMSE = \sqrt{\frac{\sum_{i = 1}^{N} {((), R_{rp, i} - R_{re, i})}^{2}}{N}}

where R re, i and R rp, i represent the i th experimental and predicted rehydration ratios, respectively.…”

Section: Methodsmentioning

confidence: 99%

See 4 more Smart Citations

Modeling and optimization of drying conditions of dog rose for preparation of a functional tea

Pashazadeh

Zannou

Koca

2020

J Food Process Engineering

Self Cite

View full text Add to dashboard Cite

Dog rose (Rosa canina L.) is a rosehip used for domestic and industrial purposes. It has great antioxidant compounds such as lycopene and phenolic compounds. In the present study, technological processing was investigated to formulate a functional tea with great nutraceutical compounds. The drying conditions were optimized to have maximum antioxidant properties. The optimum conditions were 68°C and 1 m s−1, providing the values of 40751.17 mg GAE/kg, 14.17 mg EGC E/100 g, 278.87 mmol TE/g, 44.62 mmol ISE/g, 3051.36 mg/kg, and 13.28 mg/kg for TPC, TFC, DPPH radical scavenging, FRAP, ascorbic acid, and lycopene, respectively. Midilli model described well the drying behavior of Dog rose and, Peleg, Weibull and Vega‐galves models predicted well the rehydration kinetics. A two‐factor and central composite design was applied to evaluate the effects of decoction time and solid‐to‐liquid ratio. At the optimal conditions, the Dog rose tea had 98141.28 mg GAE/kg, 2720.66 mg EGC E/100 g, 8616.57 mmol TE/g, 728.24 mmol ISE/g; and 3,358 for TPC, TFC, DPPH radical scavenging, FRAP, and ascorbic acid content, respectively. The findings showed that Dog rose fruit is a great source of nutraceutical compounds and a potential industrial product. Practical application The drying conditions to have a dried fruit with high antioxidant properties were determined. The dried Dog rose fruit is stable for long storage period and be afforded in off‐seasons. Optimization and mathematical models were successfully applied to determine the optimum drying conditions and to predict the drying and rehydration kinetics, respectively. Midilli model was the most fitted model to predict the drying kinetics, while, Peleg, Vegas‐gálves and Weibull models depicted better the rehydration kinetics at 20, 40, and 60°C. The dried fruit can be used in food, cosmetic, and pharmaceutical industries for many purposes. A functional fruit tea was formulated and optimized for its nutraceutical properties. This tea is a functional food used as a natural preventive remedy for flu, cancer, anemia, cardiovascular diseases and diabetes as well.

show abstract